{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 单发多框检测（SSD）\n",
    "我们在前几节分别介绍了边界框、锚框、多尺度目标检测和数据集，下面我们基于这些背景知识来构造一个目标检测模型：单发多框检测（single shot multibox detection，SSD）。它简单、快速，并得到了广泛应用。该模型的一些设计思想和实现细节常适用于其他目标检测模型。"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图9.4描述了单发多框检测模型的设计。它主要由一个基础网络块和若干个多尺度特征块串联而成。其中基础网络块用来从原始图像中抽取特征，因此一般会选择常用的深度卷积神经网络。单发多框检测论文中选用了在分类层之前截断的VGG，现在也常用ResNet替代。我们可以设计基础网络，使它输出的高和宽较大。这样一来，基于该特征图生成的锚框数量较多，可以用来检测尺寸较小的目标。接下来的每个多尺度特征块将上一层提供的特征图的高和宽缩小（如减半），并使特征图中每个单元在输入图像上的感受野变得更广阔。如此一来，图9.4中越靠近顶部的多尺度特征块输出的特征图越小，故而基于特征图生成的锚框也越少，加之特征图中每个单元感受野越大，因此更适合检测尺寸较大的目标。由于单发多框检测基于基础网络块和各个多尺度特征块生成不同数量和不同大小的锚框，并通过预测锚框的类别和偏移量（即预测边界框）检测不同大小的目标，因此单发多框检测是一个多尺度的目标检测模型。\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 类别预测层\n",
    "设目标的类别个数为q。每个锚框的类别个数将是q+1，其中类别0表示锚框只包含背景。在某个尺度下，设特征图的高和宽分别为h和w，如果以其中每个单元为中心生成a个锚框，那么我们需要对hwa个锚框进行分类。如果使用全连接层作为输出，很容易导致模型参数过多。回忆“网络中的网络（NiN）”一节介绍的使用卷积层的通道来输出类别预测的方法。单发多框检测采用同样的方法来降低模型复杂度。\n",
    "\n",
    "具体来说，类别预测层使用一个保持输入高和宽的卷积层。这样一来，输出和输入在特征图宽和高上的空间坐标一一对应。考虑输出和输入同一空间坐标(x,y)：输出特征图上(x,y)坐标的通道里包含了以输入特征图(x,y)坐标为中心生成的所有锚框的类别预测。因此输出通道数为a(q+1)，其中索引为i(q+1)+j（0≤j≤q）的通道代表了索引为i的锚框有关类别索引为j的预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们定义一个这样的类别预测层：指定参数a和q后，它使用一个填充为1的3×3卷积层。该卷积层的输入和输出的高和宽保持不变。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# import d2lzh as d2l\n",
    "import mxnet as mx\n",
    "from mxnet import autograd, contrib, gluon, image, init, nd\n",
    "from mxnet.gluon import loss as gloss, nn\n",
    "import time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cls_predictor(num_anchors, num_classes):\n",
    "    return nn.Conv2D(num_anchors * (num_classes + 1), kernel_size=3, padding=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 边界框预测层\n",
    "边界框预测层的设计与类别预测层的设计类似。唯一不同的是，这里需要为每个锚框预测4个偏移量，而不是q+1个类别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bbox_predictor(num_anchors):\n",
    "    return nn.Conv2D(num_anchors * 4, kernel_size=3, padding=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 连结多尺度的预测\n",
    "前面提到，单发多框检测根据多个尺度下的特征图生成锚框并预测类别和偏移量。由于每个尺度上特征图的形状或以同一单元为中心生成的锚框个数都可能不同，因此不同尺度的预测输出形状可能不同。\n",
    "\n",
    "在下面的例子中，我们对同一批量数据构造两个不同尺度下的特征图Y1和Y2，其中Y2相对于Y1来说高和宽分别减半。以类别预测为例，假设以Y1和Y2特征图中每个单元生成的锚框个数分别是5和3，当目标类别个数为10时，类别预测输出的通道数分别为5×(10+1)=55和3×(10+1)=33。预测输出的格式为(批量大小, 通道数, 高, 宽)。可以看到，除了批量大小外，其他维度大小均不一样。我们需要将它们变形成统一的格式并将多尺度的预测连结，从而让后续计算更简单。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2, 55, 20, 20), (2, 33, 10, 10))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def forward(x, block):\n",
    "    block.initialize()\n",
    "    return block(x)\n",
    "\n",
    "Y1 = forward(nd.zeros((2, 8, 20, 20)), cls_predictor(5, 10))\n",
    "Y2 = forward(nd.zeros((2, 16, 10, 10)), cls_predictor(3, 10))\n",
    "(Y1.shape, Y2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通道维包含中心相同的锚框的预测结果。我们首先将通道维移到最后一维。因为不同尺度下批量大小仍保持不变，我们可以将预测结果转成二维的(批量大小, 高×宽×通道数)的格式，以方便之后在维度1上的连结。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flatten_pred(pred):\n",
    "    return pred.transpose((0, 2, 3, 1)).flatten()\n",
    "\n",
    "def concat_preds(preds):\n",
    "    return nd.concat(*[flatten_pred(p) for p in preds], dim=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这样一来，尽管Y1和Y2形状不同，我们仍然可以将这两个同一批量不同尺度的预测结果连结在一起。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 25300)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_preds([Y1, Y2]).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 高和宽减半块\n",
    "为了在多尺度检测目标，下面定义高和宽减半块down_sample_blk。它串联了两个填充为1的3×3卷积层和步幅为2的2×2最大池化层。我们知道，填充为1的3×3卷积层不改变特征图的形状，而后面的池化层则直接将特征图的高和宽减半。由于1×2+(3−1)+(3−1)=6，输出特征图中每个单元在输入特征图上的感受野形状为6×6。可以看出，高和宽减半块使输出特征图中每个单元的感受野变得更广阔。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def down_sample_blk(num_channels):\n",
    "    blk = nn.Sequential()\n",
    "    for _ in range(2):\n",
    "        blk.add(nn.Conv2D(num_channels, kernel_size=3, padding=1),\n",
    "                nn.BatchNorm(in_channels=num_channels),\n",
    "                nn.Activation('relu'))\n",
    "    blk.add(nn.MaxPool2D(2))\n",
    "    return blk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试高和宽减半块的前向计算。可以看到，它改变了输入的通道数，并将高和宽减半。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 10, 10, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forward(nd.zeros((2, 3, 20, 20)), down_sample_blk(10)).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基础网络块\n",
    "基础网络块用来从原始图像中抽取特征。为了计算简洁，我们在这里构造一个小的基础网络。该网络串联3个高和宽减半块，并逐步将通道数翻倍。当输入的原始图像的形状为256×256时，基础网络块输出的特征图的形状为32×32。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 64, 32, 32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def base_net():\n",
    "    blk = nn.Sequential()\n",
    "    for num_filters in [16, 32, 64]:\n",
    "        blk.add(down_sample_blk(num_filters))\n",
    "    return blk\n",
    "\n",
    "forward(nd.zeros((2, 3, 256, 256)), base_net()).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 完整的模型\n",
    "单发多框检测模型一共包含5个模块，每个模块输出的特征图既用来生成锚框，又用来预测这些锚框的类别和偏移量。第一模块为基础网络块，第二模块至第四模块为高和宽减半块，第五模块使用全局最大池化层将高和宽降到1。因此第二模块至第五模块均为图9.4中的多尺度特征块。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_blk(i):\n",
    "    if i == 0:\n",
    "        blk = base_net()\n",
    "    elif i == 4:\n",
    "        blk = nn.GlobalMaxPool2D()\n",
    "    else:\n",
    "        blk = down_sample_blk(128)\n",
    "    return blk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，我们定义每个模块如何进行前向计算。与之前介绍的卷积神经网络不同，这里不仅返回卷积计算输出的特征图Y，还返回根据Y生成的当前尺度的锚框，以及基于Y预测的锚框类别和偏移量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def blk_forward(X, blk, size, ratio, cls_predictor, bbox_predictor):\n",
    "    Y = blk(X)\n",
    "    anchors = contrib.ndarray.MultiBoxPrior(Y, sizes=size, ratios=ratio)\n",
    "    cls_preds = cls_predictor(Y)\n",
    "    bbox_preds = bbox_predictor(Y)\n",
    "    return (Y, anchors, cls_preds, bbox_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们提到，图9.4中较靠近顶部的多尺度特征块用来检测尺寸较大的目标，因此需要生成较大的锚框。我们在这里先将0.2到1.05之间均分5份，以确定不同尺度下锚框大小的较小值0.2、0.37、0.54等，再按√0.2×0.37=0.272、√0.37×0.54=0.447等来确定不同尺度下锚框大小的较大值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sizes = [[0.2, 0.272], [0.37, 0.447], [0.54, 0.619], [0.71, 0.79],\n",
    "         [0.88, 0.961]]\n",
    "ratios = [[1, 2, 0.5]] * 5\n",
    "num_anchors = len(sizes[0]) + len(ratios[0]) - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，我们就可以定义出完整的模型TinySSD了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TinySSD(nn.Block):\n",
    "    def __init__(self, num_classes, **kwargs):\n",
    "        super(TinySSD, self).__init__(**kwargs)\n",
    "        self.num_classes = num_classes\n",
    "        for i in range(5):\n",
    "            # 即赋值语句self.blk_i = get_blk(i)\n",
    "            setattr(self, 'blk_%d' % i, get_blk(i))\n",
    "            setattr(self, 'cls_%d' % i, cls_predictor(num_anchors, num_classes))\n",
    "            setattr(self, 'bbox_%d' % i, bbox_predictor(num_anchors))\n",
    "\n",
    "    def forward(self, X):\n",
    "        anchors, cls_preds, bbox_preds = [None] * 5, [None] * 5, [None] * 5\n",
    "        for i in range(5):\n",
    "            # getattr(self, 'blk_%d' % i)即访问self.blk_i\n",
    "            X, anchors[i], cls_preds[i], bbox_preds[i] = blk_forward(\n",
    "                X, getattr(self, 'blk_%d' % i), sizes[i], ratios[i],\n",
    "                getattr(self, 'cls_%d' % i), getattr(self, 'bbox_%d' % i))\n",
    "        # 将5个模块产生的anchors以及类别预测和位置预测全部拼接后返回\n",
    "        # reshape函数中的0表示保持批量大小不变\n",
    "        return (nd.concat(*anchors, dim=1),\n",
    "                concat_preds(cls_preds).reshape(\n",
    "                    (0, -1, self.num_classes + 1)), concat_preds(bbox_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们创建单发多框检测模型实例并对一个高和宽均为256像素的小批量图像X做前向计算。我们在之前验证过，第一模块输出的特征图的形状为32×32。由于第二至第四模块为高和宽减半块、第五模块为全局池化层，并且以特征图每个单元为中心生成4个锚框，每个图像在5个尺度下生成的锚框总数为(32^2+16^2+8^2+4^2+1)×4=5444。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output anchors: (1, 5444, 4)\n",
      "output class preds: (32, 5444, 2)\n",
      "output bbox preds: (32, 21776)\n"
     ]
    }
   ],
   "source": [
    "net = TinySSD(num_classes=1)\n",
    "net.initialize()\n",
    "X = nd.zeros((32, 3, 256, 256))\n",
    "anchors, cls_preds, bbox_preds = net(X)\n",
    "\n",
    "print('output anchors:', anchors.shape)\n",
    "print('output class preds:', cls_preds.shape)\n",
    "print('output bbox preds:', bbox_preds.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型\n",
    "#### 读取数据集和初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "data_dir = 'Datasets/pikachu'\n",
    "\n",
    "def load_data_pikachu(batch_size, edge_size=256):  # edge_size：输出图像的宽和高\n",
    "    train_iter = image.ImageDetIter(\n",
    "        path_imgrec=os.path.join(data_dir, 'train.rec'),\n",
    "        path_imgidx=os.path.join(data_dir, 'train.idx'),\n",
    "        batch_size=batch_size,\n",
    "        data_shape=(3, edge_size, edge_size),  # 输出图像的形状\n",
    "        shuffle=False,  # 以随机顺序读取数据集\n",
    "#         rand_crop=1,  # 随机裁剪的概率为1\n",
    "        min_object_covered=0.95, max_attempts=200)\n",
    "    val_iter = image.ImageDetIter(\n",
    "        path_imgrec=os.path.join(data_dir, 'val.rec'), batch_size=batch_size,\n",
    "        data_shape=(3, edge_size, edge_size), shuffle=False)\n",
    "    return train_iter, val_iter\n",
    "\n",
    "# batch_size = 32\n",
    "batch_size = 20\n",
    "train_iter, val_iter = load_data_pikachu(batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在皮卡丘数据集中，目标的类别数为1。定义好模型以后，我们需要初始化模型参数并定义优化算法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gpu(0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def try_gpu():\n",
    "    try:\n",
    "        ctx = mx.gpu()\n",
    "        _ = nd.zeros((1,), ctx=ctx)\n",
    "    except mx.base.MXNetError:\n",
    "        ctx = mx.cpu()\n",
    "    return ctx\n",
    "\n",
    "ctx, net = try_gpu(), TinySSD(num_classes=1)\n",
    "net.initialize(init=init.Xavier(), ctx=ctx)\n",
    "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.2, 'wd': 5e-4})\n",
    "ctx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 定义损失函数和评价函数\n",
    "目标检测有两个损失：一是有关锚框类别的损失，我们可以重用之前图像分类问题里一直使用的交叉熵损失函数；二是有关正类锚框偏移量的损失。预测偏移量是一个回归问题，但这里不使用前面介绍过的平方损失，而使用L1范数损失，即预测值与真实值之间差的绝对值。掩码变量bbox_masks令负类锚框和填充锚框不参与损失的计算。最后，我们将有关锚框类别和偏移量的损失相加得到模型的最终损失函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls_loss = gloss.SoftmaxCrossEntropyLoss()\n",
    "bbox_loss = gloss.L1Loss()\n",
    "\n",
    "def calc_loss(cls_preds, cls_labels, bbox_preds, bbox_labels, bbox_masks):\n",
    "    cls = cls_loss(cls_preds, cls_labels)\n",
    "    bbox = bbox_loss(bbox_preds * bbox_masks, bbox_labels * bbox_masks)\n",
    "    # return cls + bbox\n",
    "    return cls + bbox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以沿用准确率评价分类结果。因为使用了L1范数损失，我们用平均绝对误差评价边界框的预测结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cls_eval(cls_preds, cls_labels):\n",
    "    # 由于类别预测结果放在最后一维，argmax需要指定最后一维\n",
    "    return (cls_preds.argmax(axis=-1) == cls_labels).sum().asscalar()\n",
    "\n",
    "def bbox_eval(bbox_preds, bbox_labels, bbox_masks):\n",
    "    return ((bbox_labels - bbox_preds) * bbox_masks).abs().sum().asscalar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 训练模型\n",
    "在训练模型时，我们需要在模型的前向计算过程中生成多尺度的锚框anchors，并为每个锚框预测类别cls_preds和偏移量bbox_preds。之后，我们根据标签信息Y为生成的每个锚框标注类别cls_labels和偏移量bbox_labels。最后，我们根据类别和偏移量的预测和标注值计算损失函数。为了代码简洁，这里没有评价测试数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  1, class err 1.54e-02, bbox mae 7.66e-04, time 25.3 sec\n",
      "epoch  2, class err 4.60e-04, bbox mae 6.46e-04, time 21.5 sec\n",
      "epoch  3, class err 4.04e-04, bbox mae 5.78e-04, time 21.6 sec\n",
      "epoch  4, class err 4.08e-04, bbox mae 5.23e-04, time 21.5 sec\n",
      "epoch  5, class err 4.10e-04, bbox mae 4.79e-04, time 21.5 sec\n",
      "epoch  6, class err 4.09e-04, bbox mae 4.44e-04, time 21.6 sec\n",
      "epoch  7, class err 4.07e-04, bbox mae 4.17e-04, time 21.6 sec\n",
      "epoch  8, class err 4.05e-04, bbox mae 3.98e-04, time 21.6 sec\n",
      "epoch  9, class err 4.01e-04, bbox mae 3.85e-04, time 21.7 sec\n",
      "epoch 10, class err 4.00e-04, bbox mae 3.75e-04, time 21.6 sec\n",
      "epoch 11, class err 3.99e-04, bbox mae 3.67e-04, time 21.6 sec\n",
      "epoch 12, class err 3.98e-04, bbox mae 3.60e-04, time 21.7 sec\n",
      "epoch 13, class err 3.96e-04, bbox mae 3.54e-04, time 21.7 sec\n",
      "epoch 14, class err 3.94e-04, bbox mae 3.49e-04, time 21.7 sec\n",
      "epoch 15, class err 3.91e-04, bbox mae 3.44e-04, time 21.7 sec\n",
      "epoch 16, class err 3.88e-04, bbox mae 3.38e-04, time 21.6 sec\n",
      "epoch 17, class err 3.88e-04, bbox mae 3.34e-04, time 22.1 sec\n",
      "epoch 18, class err 3.86e-04, bbox mae 3.29e-04, time 21.7 sec\n",
      "epoch 19, class err 3.84e-04, bbox mae 3.25e-04, time 21.6 sec\n",
      "epoch 20, class err 3.82e-04, bbox mae 3.21e-04, time 21.6 sec\n",
      "epoch 21, class err 3.81e-04, bbox mae 3.17e-04, time 22.1 sec\n",
      "epoch 22, class err 3.79e-04, bbox mae 3.14e-04, time 21.6 sec\n",
      "epoch 23, class err 3.78e-04, bbox mae 3.10e-04, time 21.5 sec\n",
      "epoch 24, class err 3.78e-04, bbox mae 3.06e-04, time 21.6 sec\n",
      "epoch 25, class err 3.78e-04, bbox mae 3.03e-04, time 21.7 sec\n",
      "epoch 26, class err 3.76e-04, bbox mae 3.00e-04, time 21.6 sec\n",
      "epoch 27, class err 3.76e-04, bbox mae 2.97e-04, time 21.5 sec\n",
      "epoch 28, class err 3.75e-04, bbox mae 2.94e-04, time 21.9 sec\n",
      "epoch 29, class err 3.73e-04, bbox mae 2.91e-04, time 21.6 sec\n",
      "epoch 30, class err 3.72e-04, bbox mae 2.88e-04, time 21.7 sec\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(30):\n",
    "    acc_sum, mae_sum, n, m = 0.0, 0.0, 0, 0\n",
    "    train_iter.reset()  # 从头读取数据\n",
    "    start = time.time()\n",
    "    for batch in train_iter:\n",
    "        X = batch.data[0].as_in_context(ctx)\n",
    "        Y = batch.label[0].as_in_context(ctx)\n",
    "        with autograd.record():\n",
    "            # 生成多尺度的锚框，为每个锚框预测类别和偏移量\n",
    "            anchors, cls_preds, bbox_preds = net(X)\n",
    "            # 为每个锚框标注类别和偏移量\n",
    "            bbox_labels, bbox_masks, cls_labels = contrib.nd.MultiBoxTarget(\n",
    "                anchors, Y, cls_preds.transpose((0, 2, 1)))\n",
    "            # 根据类别和偏移量的预测和标注值计算损失函数\n",
    "            l = calc_loss(cls_preds, cls_labels, bbox_preds, bbox_labels,\n",
    "                          bbox_masks)\n",
    "        l.backward()\n",
    "        trainer.step(batch_size)\n",
    "        acc_sum += cls_eval(cls_preds, cls_labels)\n",
    "        n += cls_labels.size\n",
    "        mae_sum += bbox_eval(bbox_preds, bbox_labels, bbox_masks)\n",
    "        m += bbox_labels.size \n",
    "\n",
    "    if (epoch + 1) % 1 == 0:   \n",
    "        print('epoch %2d, class err %.2e, bbox mae %.2e, time %.1f sec' % (\n",
    "            epoch + 1, 1 - acc_sum / n, mae_sum / m, time.time() - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 预测目标\n",
    "在预测阶段，我们希望能把图像里面所有我们感兴趣的目标检测出来。下面读取测试图像，将其变换尺寸，然后转成卷积层需要的四维格式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = image.imread('Datasets/pikachu.jpg')\n",
    "feature = image.imresize(img, 256, 256).astype('float32')\n",
    "X = feature.transpose((2, 0, 1)).expand_dims(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们通过MultiBoxDetection函数根据锚框及其预测偏移量得到预测边界框，并通过非极大值抑制移除相似的预测边界框。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(X):\n",
    "    anchors, cls_preds, bbox_preds = net(X.as_in_context(ctx))\n",
    "    cls_probs = cls_preds.softmax().transpose((0, 2, 1))\n",
    "    output = contrib.nd.MultiBoxDetection(cls_probs, bbox_preds, anchors)\n",
    "    idx = [i for i, row in enumerate(output[0]) if row[0].asscalar() != -1]\n",
    "    return output[0, idx]\n",
    "\n",
    "output = predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后，我们将置信度不低于0.3的边界框筛选为最终输出用以展示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "def use_svg_display():\n",
    "    display.set_matplotlib_formats('svg') # 用矢量图显示\n",
    "\n",
    "def set_figsize(figsize=(3.5, 2.5)):\n",
    "    use_svg_display()\n",
    "    plt.rcParams['figure.figsize'] = figsize # 设置图的尺寸\n",
    "\n",
    "set_figsize((5, 5))\n",
    "\n",
    "def bbox_to_rect(bbox, color):\n",
    "    # 将边界框(左上x, 左上y, 右下x, 右下y)格式转换成matplotlib格式：((左上x, 左上y), 宽, 高)\n",
    "    return plt.Rectangle(xy=(bbox[0],bbox[1]), width=bbox[2]-bbox[0], height=bbox[3]-bbox[1], fill=False, edgecolor=color, linewidth=2)\n",
    "\n",
    "def show_bboxes(axes, bboxes, labels=None, colors=None):\n",
    "    # bboxes = [[x,y,x,y], [x,y,x,y],...,[x,y,x,y]]\n",
    "    def _make_list(obj, default_values=None):\n",
    "        if obj is None:\n",
    "            obj = default_values\n",
    "        elif not isinstance(obj, (list, tuple)):\n",
    "            obj = [obj]\n",
    "        return obj\n",
    "    \n",
    "    labels = _make_list(labels)\n",
    "    colors = _make_list(colors, ['b', 'g', 'r', 'm', 'c'])\n",
    "    for i, bbox in enumerate(bboxes):\n",
    "        color = colors[i % len(colors)]\n",
    "        rect = bbox_to_rect(bbox.asnumpy(), color)\n",
    "        axes.add_patch(rect)\n",
    "        if labels and len(labels) > i:\n",
    "            text_color = 'k' if color == 'w' else 'w' # 用白色显示文字，但是如果框的颜色color已经是白色，则文字用k(接近黑色)\n",
    "            axes.text(rect.xy[0], rect.xy[1], labels[i], va='center', ha='center', fontsize=5, color=text_color, bbox=dict(facecolor=color,lw=0))\n",
    "\n",
    "def display(img, output, threshold):\n",
    "    fig = plt.imshow(img.asnumpy())\n",
    "    for row in output:\n",
    "        score = row[1].asscalar()\n",
    "        if score < threshold:\n",
    "            continue\n",
    "        h, w = img.shape[0:2]\n",
    "        bbox = [row[2:6] * nd.array((w, h, w, h), ctx=row.context)]\n",
    "        show_bboxes(fig.axes, bbox, '%.2f' % score, 'w')\n",
    "\n",
    "display(img, output, threshold=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 小结\n",
    "1、单发多框检测是一个多尺度的目标检测模型。该模型基于基础网络块和各个多尺度特征块生成不同数量和不同大小的锚框，并通过预测锚框的类别和偏移量检测不同大小的目标。\n",
    "\n",
    "2、单发多框检测在训练中根据类别和偏移量的预测和标注值计算损失函数。"
   ]
  },
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 损失函数\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 252x180 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "set_figsize()\n",
    "sigmas = [10, 1, 0.5]\n",
    "lines = ['-', '--', '-.']\n",
    "x = nd.arange(-2, 2, 0.1)\n",
    "\n",
    "for l, s in zip(lines, sigmas):\n",
    "    y = nd.smooth_l1(x, scalar=s)\n",
    "    plt.plot(x.asnumpy(), y.asnumpy(), l, label='sigma=%.1f' % s)\n",
    "plt.legend();"
   ]
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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    "\n",
    "def focal_loss(gamma, x):\n",
    "    return -(1 - x) ** gamma * x.log()\n",
    "\n",
    "x = nd.arange(0.01, 1, 0.01)\n",
    "for l, gamma in zip(lines, [0, 1, 5]):\n",
    "    y = plt.plot(x.asnumpy(), focal_loss(gamma, x).asnumpy(), l,\n",
    "                     label='gamma=%.1f' % gamma)\n",
    "plt.legend();"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1、当目标在图像中占比较小时，模型通常会采用比较大的输入图像尺寸。\n",
    "\n",
    "2、为锚框标注类别时，通常会产生大量的负类锚框。可以对负类锚框进行采样，从而使数据类别更加平衡。这个可以通过设置MultiBoxTarget函数的negative_mining_ratio参数来完成。\n",
    "\n",
    "3、在损失函数中为有关锚框类别和有关正类锚框偏移量的损失分别赋予不同的权重超参数。"
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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